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Multivariate dependence modeling based on comonotonic factors

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  • Hua, Lei
  • Joe, Harry

Abstract

Comonotonic latent variables are introduced into general factor models, in order to allow non-linear transformations of latent factors, so that various multivariate dependence structures can be captured. Through decomposing each univariate marginal into several components, and letting some components belong to different sets of comonotonic latent variables, a great variety of multivariate models can be constructed, and their induced copulas can be used to model various multivariate dependence structures. The paper focuses on an extension of Archimedean copulas constructed by Laplace Transforms of positive random variables. The corresponding comonotonic factor models with one set of comonotonic latent variables and multiple sets of comonotonic latent variables are studied. In particular, we propose several parametric comonotonic factor models that are useful in accommodating both within-group and between-group dependence with possible asymmetric tail dependence. Numerical methods for estimation with the resulting copula models are discussed. There is an application using a dataset of body composition measurements to demonstrate the usefulness of the proposed parsimonious dependence models.

Suggested Citation

  • Hua, Lei & Joe, Harry, 2017. "Multivariate dependence modeling based on comonotonic factors," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 317-333.
    • Handle: RePEc:eee:jmvana:v:155:y:2017:i:c:p:317-333
    DOI: 10.1016/j.jmva.2017.01.008
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    References listed on IDEAS

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    1. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    2. Dong Hwan Oh & Andrew J. Patton, 2017. "Modeling Dependence in High Dimensions With Factor Copulas," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 139-154, January.
    3. Joe, Harry & Sang, Peijun, 2016. "Multivariate models for dependent clusters of variables with conditional independence given aggregation variables," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 114-132.
    4. Karl Holzinger & Frances Swineford, 1937. "The Bi-factor method," Psychometrika, Springer;The Psychometric Society, vol. 2(1), pages 41-54, March.
    5. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
    6. Anastasios Panagiotelis & Claudia Czado & Harry Joe, 2012. "Pair Copula Constructions for Multivariate Discrete Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1063-1072, September.
    7. Dhaene, J. & Denuit, M. & Goovaerts, M. J. & Kaas, R. & Vyncke, D., 2002. "The concept of comonotonicity in actuarial science and finance: theory," Insurance: Mathematics and Economics, Elsevier, vol. 31(1), pages 3-33, August.
    8. Krupskii, Pavel & Joe, Harry, 2015. "Structured factor copula models: Theory, inference and computation," Journal of Multivariate Analysis, Elsevier, vol. 138(C), pages 53-73.
    9. Dhaene, J. & Denuit, M. & Goovaerts, M. J. & Kaas, R. & Vyncke, D., 2002. "The concept of comonotonicity in actuarial science and finance: applications," Insurance: Mathematics and Economics, Elsevier, vol. 31(2), pages 133-161, October.
    10. Hua, Lei, 2015. "Tail negative dependence and its applications for aggregate loss modeling," Insurance: Mathematics and Economics, Elsevier, vol. 61(C), pages 135-145.
    11. Xiaolin Luo & Pavel V. Shevchenko, 2007. "The t copula with Multiple Parameters of Degrees of Freedom: Bivariate Characteristics and Application to Risk Management," Papers 0710.3959, arXiv.org, revised Feb 2010.
    12. Xiaolin Luo & Pavel Shevchenko, 2010. "The t copula with multiple parameters of degrees of freedom: bivariate characteristics and application to risk management," Quantitative Finance, Taylor & Francis Journals, vol. 10(9), pages 1039-1054.
    13. McNeil, Alexander J. & Neslehová, Johanna, 2010. "From Archimedean to Liouville copulas," Journal of Multivariate Analysis, Elsevier, vol. 101(8), pages 1772-1790, September.
    14. Yang, Xipei & Frees, Edward W. & Zhang, Zhengjun, 2011. "A generalized beta copula with applications in modeling multivariate long-tailed data," Insurance: Mathematics and Economics, Elsevier, vol. 49(2), pages 265-284, September.
    15. Krupskii, Pavel & Joe, Harry, 2013. "Factor copula models for multivariate data," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 85-101.
    16. Hua, Lei & Joe, Harry, 2011. "Tail order and intermediate tail dependence of multivariate copulas," Journal of Multivariate Analysis, Elsevier, vol. 102(10), pages 1454-1471, November.
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    Cited by:

    1. Hua, Lei & Polansky, Alan & Pramanik, Paramahansa, 2019. "Assessing bivariate tail non-exchangeable dependence," Statistics & Probability Letters, Elsevier, vol. 155(C), pages 1-1.
    2. Paramahansa Pramanik, 2024. "Dependence on Tail Copula," J, MDPI, vol. 7(2), pages 1-26, April.
    3. Perreault, Samuel & Duchesne, Thierry & Nešlehová, Johanna G., 2019. "Detection of block-exchangeable structure in large-scale correlation matrices," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 400-422.

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